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This study investigates applying machine learning (ML) algorithms to estimate seismic fragility curves of reinforced concrete moment-resisting frames. The goal is to achieve computationally efficient estimations while maintaining accuracy compared to traditional methods. Various multi-classification algorithms are explored for this purpose. A greedy search method identifies key fragility features (structure period, soil shear wave velocity, ground motion intensity). Analysis shows that the decision tree method strikes an optimal balance between accuracy and speed. ML-based fragility curves closely align with analytical results, with area ratios under 10%, mean absolute error (MAE) below 0.06, and root mean square error (RMSE) below 0.082. [ABSTRACT FROM AUTHOR]

Masonry infill walls are commonly used as partitions and exterior walls in many countries. Generally, the masonry wall is executed without any gap from the frame, which leads to the interaction between the structural frame and the infill wall. Interaction between the structural frame and the masonry infill wall can damage the frame and the infill wall. Therefore, it is necessary to find a solution to improve the performance of masonry infill walls in the structural frame. Isolation of the masonry infill wall from the surrounding frame by polymeric material is the idea of this paper to decrease the damage to the structure and masonry infill. In this essay, Finite element models of steel infill frame and isolated infill frame subjected to In-Plane cyclic loading are developed in ABAQUS. For this purpose, three one-bay, one-story masonry-infilled steel frames with different frame ratios of height to the length (H/B) isolated by different polymeric materials with various thicknesses were investigated. Isolation of masonry infill can reduce the base reaction about 25%. In the Isolated Infill wall, the drift's amount increases about two times compared with the unseparated infill wall. Therefore, it damages the masonry infills up to moderate drifts, while full interaction is still in place drifts are large. Also, infill walls isolated by a softer polymer, have better performance. In brief, isolation of infill wall using polymeric materials improves the behavior of the infill and frame. [ABSTRACT FROM AUTHOR]

Soil-structure interaction (SSI) could affect the seismic response of structures. Since liquid storage tanks are vital structures and must continue their operation under severe earthquakes, their seismic behavior should be studied. Accordingly, the seismic response of two types of steel liquid storage tanks (namely, broad and slender, with aspect ratios of height to radius equal to 0.6 and 1.85) founded on half-space soil is scrutinized under different earthquake ground motions. For a better comparison, the six considered ground motions are classified, based on their pulse-like characteristics, into two groups, named far and near fault ground motions. To model the liquid storage tanks, the simplified mass-spring model is used and the liquid is modeled as two lumped masses known as sloshing and impulsive, and the interaction of fluid and structure is considered using two coupled springs and dashpots. The SSI effect, also, is considered using a coupled spring and dashpot. Additionally, four types of soils are used to consider a wide variety of soil properties. To this end, after deriving the equations of motion, MATLAB programming is employed to obtain the time history responses. Results show that although the SSI effect leads to a decrease in the impulsive displacement, overturning moment, and normalized base shear, the sloshing (or convective) displacement is not affected by such effects due to its long period. [ABSTRACT FROM AUTHOR]